The present embodiments relate to an apparatus and a method for planning a treatment beam aimed at one or more target regions.
In addition to beam therapy, chemotherapy and surgical removal, ablation has developed into an increasingly important minimal-invasion method in oncology, allowing the complete removal of tumors and thus the prevention of any further spread of the pathological tissue.
DE 10 2011 075 738 A1 discloses a method and an apparatus for ascertaining a robust ablation program for ablation of a tissue region. The program includes one or more ablation parameters that define the performance of the ablation and have associated predetermined parameter values. Based on first probability distributions for first predetermined parameter values, a second probability distribution of expected second ablation regions is ascertained. Second predetermined parameter values of the ablation parameters are chosen such that a third ablation region is ablatable. The third ablation region is ascertained from the second probability distribution and the expected second ablation regions with the optimization or maximization of at least one prespecified variable.
In beam therapy, X-rays, electron beams, laser beams or particle beams irradiate diseased tissue, among others. In recent years, particle therapy, for example, has become an established method for treating tissue (e.g., tumors), although irradiation methods, as are used as part of particle therapy, may also be employed in non-therapeutic areas such as, for example, the irradiation of phantoms or non-living bodies for research, in the irradiation of materials, etc.
In particle therapy, particles are generated (e.g., ions such as protons, carbon ions or other ion types). The particles are accelerated to high energies in an accelerator, shaped to form a particle beam, and subsequently aimed at the tissue to be irradiated. The particles penetrate the tissue to be irradiated and release energy in a circumscribed area. The depth of penetration of the particle beam into the tissue to be irradiated is primarily dependent on the energy of the particle beam. The higher the energy of the particle beam, the deeper the penetration of the particles into the tissue to be irradiated.
The total amount of radiation to be delivered by the irradiation apparatus is to be determined during the beam therapy planning process.
In beam therapy, the beam therapy plan is calculated and may be displayed on a display/monitor, without taking an uncertainty into consideration. Such uncertainty may result, for example, from errors/variations in the Hounsfield units, from adjustment errors or from contour errors/contour changes.
Hounsfield variations may result from a changed anatomy of the patient, while Hounsfield errors occur as a result of inaccuracies during image capturing or reconstruction. Contour changes may result from the tumor being located at an unexpected location, while contour errors occur if the tumor has not been correctly plotted.
In order to incorporate such uncertainty in the planning process of the beam therapy, the robustness of the planning is evaluated. It is the aim of a radiation therapy plan to determine the ideal radiation dose (e.g., distribution) under the aspect of having as great a robustness as possible. The parameters radiation dose and robustness are weighed against each other.
The following radiation dose planning methods are known: 1. a representation of a color-coded probability when an image voxel reaches or exceeds a certain dose (e.g., see “Simulation and visualization of dose uncertainties due to interfractional organ motion,” Phys. Med. Biol. 2006, pages 2237-2252); and 2. a series of dose-volume curves (e.g., see Wei Chen et al., “Including robustness in multi-criteria optimization for intensity-modulated proton therapy,” Phys. Med. Biol. 57, IOP publishing, pages 591-608; Wei Liu et al., “Robust optimization of intensity modulated proton therapy,” Medical Physics, vol. 39, no. 2, Am. Assoc. Phys. Med., pages 1079-1091; and Jan Unkelbach et al., “Reducing the sensitivity of IMPT treatment plans to set up errors and range uncertainties via probabilistic treatment planning,” Med. Phys. 36 (1), Am. Assoc. Phys. Med., pages 149-163).